Rebello RJ, Oing C, Knudsen KE et al (2021) Prostate cancer. Nat Rev Dis Prim 7:1–27

Google Scholar 

Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389:815–822

Article  PubMed  Google Scholar 

Goldenberg SL, Nir G, Salcudean SE (2019) A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 16:391–403

Article  PubMed  Google Scholar 

Guiot J, Vaidyanathan A, Deprez L et al (2022) A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev 42:426–440

Article  PubMed  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

Article  PubMed  Google Scholar 

Hunter B, Hindocha S, Lee RW (2022) The role of artificial intelligence in early cancer diagnosis. Cancers (Basel) 14:1524

Article  PubMed  CAS  Google Scholar 

Koh D-M, Papanikolaou N, Bick U et al (2022) Artificial intelligence and machine learning in cancer imaging. Commun Med 2:1–14

Article  Google Scholar 

van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 11:1–16

Google Scholar 

Demircioğlu A (2022) Evaluation of the dependence of radiomic features on the machine learning model. Insights Imaging 13:1–11

Article  Google Scholar 

Ibrahim A, Primakov S, Beuque M et al (2021) Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework. Methods 188:20–29

Article  PubMed  CAS  Google Scholar 

Zhang YP, Zhang XY, Cheng YT et al (2023) Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 10:1–33

Google Scholar 

Zhang W, Guo Y, Jin Q, Zhang W, Guo Y, Jin Q (2023) Radiomics and its feature selection: a review. Symmetry 15:1834

Article  CAS  Google Scholar 

Demircioǧlu A (2022) Benchmarking feature selection methods in radiomics. Invest Radiol 57:433–443

Article  PubMed  Google Scholar 

Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:1–11

Article  Google Scholar 

Decoux A, Duron L, Habert P et al (2023) Comparative performances of machine learning algorithms in radiomics and impacting factors. Sci Rep 13:1–10

Article  Google Scholar 

Rodrigues A, Santinha J, Galvão B, Matos C, Couto FM, Papanikolaou N (2021) Prediction of prostate cancer disease aggressiveness using bi-parametric MRI radiomics. Cancers (Basel) 13:6065

Article  PubMed  Google Scholar 

Kumar A, Jha AK, Agarwal JP et al (2023) Machine-learning-based radiomics for classifying glioma grade from magnetic resonance images of the brain. J Pers Med 13:920

Article  PubMed  PubMed Central  Google Scholar 

Corso F, Tini G, Lo Presti G et al (2021) The challenge of choosing the best classification method in radiomic analyses: recommendations and applications to lung cancer CT images. Cancers (Basel) 13:3088

Article  PubMed  Google Scholar 

Chen C, Zheng A, Ou X, Wang J, Ma X (2020) Comparison of radiomics-based machine-learning classifiers in diagnosis of glioblastoma from primary central nervous system lymphoma. Front Oncol 10:1151

Article  PubMed  PubMed Central  Google Scholar 

Destito M, Marzullo A, Leone R et al (2022) Radiomics-based machine learning model for predicting overall and progression-free survival in rare cancer: a case study for primary CNS lymphoma patients. Bioengineering 10:285

Article  Google Scholar 

Schwier M, van Griethuysen J, Vangel MG et al (2019) Repeatability of multiparametric prostate MRI radiomics features. Sci Rep 9:1–16

Article  Google Scholar 

Donisi L, Cesarelli G, Castaldo A et al (2021) A combined radiomics and machine learning approach to distinguish clinically significant prostate lesions on a publicly available MRI dataset. J Imaging 7:215

Article  PubMed  PubMed Central  Google Scholar 

Isaksson LJ, Raimondi S, Botta F et al (2020) Effects of MRI image normalization techniques in prostate cancer radiomics. Phys Medica 71:7–13

Article  Google Scholar 

Bernatz S, Ackermann J, Mandel P et al (2020) Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features. Eur Radiol 30:6757–6769

Article  PubMed  PubMed Central  Google Scholar 

Bleker J, Kwee TC, Dierckx RAJO, de Jong IJ, Huisman H, Yakar D (2020) Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer. Eur Radiol 30:1313–1324

Article  PubMed  Google Scholar 

Chen T, Zhang Z, Tan S et al (2022) MRI based radiomics compared with the PI-RADS V2.1 in the prediction of clinically significant prostate cancer: biparametric vs multiparametric MRI. Front Oncol 11:792456

Article  PubMed  PubMed Central  Google Scholar 

ProCAncer-I (2023) An AI platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum. https://www.procancer-i.eu/. Accessed 13 Nov 2023

Samuel G, Armato I, Huisman H et al (2018) PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J Med Imaging 5:1

Google Scholar 

Cuocolo R, Stanzione A, Castaldo A, De Lucia DR, Imbriaco M (2021) Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset. Eur J Radiol 138:109647

Article  PubMed  Google Scholar 

Cuocolo R, Comelli A, Stefano A et al (2021) Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. J Magn Reson Imaging 54:452–459

Article  PubMed  Google Scholar 

Mottet N, Bellmunt J, Bolla M et al (2017) EAU-ESTRO-SIOG guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 71:618–629

Article  PubMed  Google Scholar 

Briganti A, Fossati N, Catto JWF et al (2018) Active surveillance for low-risk prostate cancer: the European Association of Urology Position in 2018. Eur Urol 74:357–368

Article  PubMed  Google Scholar 

Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320

Article  PubMed  PubMed Central  Google Scholar 

Van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

Article  PubMed  PubMed Central  Google Scholar 

Duron L, Balvay D, Vande Perre S et al (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 14:e0213459

Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378

Article  PubMed  Google Scholar 

Haynes W (2013) Benjamini–Hochberg method. In: Encyclopedia of systems biology. Springer, Berlin, p 78

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288

Article  Google Scholar 

Gretton A, Bousquet O, Smola A, Scḧlkopf B (2005) Measuring statistical dependence with Hilbert-Schmidt norms. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 3734 LNAI, pp 63–77

Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20:1124–1137

Article  PubMed  PubMed Central  Google Scholar 

Kocak B, Baessler B, Bakas S et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:1–13

Article  Google Scholar 

Kocak B, Akinci D’Antonoli T, Mercaldo N et al (2024) METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 15:1–18